scholarly journals A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4688 ◽  
Author(s):  
André Eugênio Lazzaretti ◽  
Clayton Hilgemberg da Costa ◽  
Marcelo Paludetto Rodrigues ◽  
Guilherme Dan Yamada ◽  
Gilberto Lexinoski ◽  
...  
Keyword(s):  
Fault Detection ◽  
Monitoring System ◽  
Environmental Variables ◽  
Energy Use ◽  
Short Circuit ◽  
Environmental Parameters ◽  
Open Circuit ◽  
Ann Model ◽  
Pv System ◽  
Fault Detection And Classification

Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same MS, we propose a recursive linear model to detect faults in the system, while using irradiance and temperature on the PV panel as input signals and power as output. The accuracy of the fault detection for a 5 kW power plant used in the test is 93.09%, considering 16 days and around 143 hours of faults in different conditions. Once a fault is detected by this model, a machine-learning-based method classifies each fault in the following cases: short-circuit, open-circuit, partial shadowing, and degradation. Using the same days and faults applied in the detection module, the accuracy of the classification stage is 95.44% for an Artificial Neural Network (ANN) model. By combining detection and classification, the overall accuracy is 92.64%. Such a result represents an original contribution of this work, since other related works do not present the integration of a fault detection and classification approach with an embedded PV plant monitoring system, allowing for the online identification and classification of different PV faults, besides real-time and historical monitoring of electrical and environmental parameters of the plant.

scholarly journals Stator winding fault detection of induction generator based wind turbine using ANN

2020 ◽  
Vol 19 (1) ◽  
pp. 126
Author(s):  
N. F. Fadzail ◽  
S. Mat Zali ◽  
M. A. Khairudin ◽  
N. H. Hanafi
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Short Circuit ◽  
Open Circuit ◽  
Induction Generator ◽  
Stator Winding ◽  
Ann Model ◽  
Induction Generators ◽  
Artificial Neural Network Ann

This paper presents a stator winding faults detection in induction generator based wind turbines by using artificial neural network (ANN). Stator winding faults of induction generators are the most common fault found in wind turbines. This fault may lead to wind turbine failure. Therefore, fault detection in induction generator based wind turbines is vital to increase the reliability of wind turbines. In this project, the mathematical model of induction generator based wind turbine was developed in MATLAB Simulink. The value of impedance in the induction generators was changed to simulate the inter-turn short circuit and open circuit faults. The simulated responses of the induction generators were used as inputs in the ANN model for fault detection procedures. A set of data was taken under different conditions, i.e. normal condition, inter-turn short circuit and open circuit faults as inputs for the ANN model. The target outputs of the ANN model were set as ‘0’ or ‘1’, based on the fault conditions. Results obtained showed that the ANN model can detect different types of faults based on the output values of the ANN model. In conclusion, the stator winding faults detection procedure for induction generator based wind turbines by using ANN was successfully developed.

scholarly journals Short and open circuit faults study in the PV system inverter

2021 ◽  
Vol 12 (3) ◽  
pp. 1764
Author(s):  
Mohammed Bouzidi ◽  
Abdelkader Harrouz ◽  
Tadj Mohammed ◽  
Smail Mansouri
Keyword(s):  
Short Circuit ◽  
Electric Utility ◽  
Open Circuit ◽  
Photovoltaic System ◽  
Pv System ◽  
Simple Method ◽  
Pv Systems ◽  
Pv Array ◽  
Main Component ◽  
Types Of Faults

<p>The inverter is the principal part of the photovoltaic (PV) systems that assures the direct current/alternating current (DC/AC) conversion (PV array is connected directly to an inverter that converts the DC energy produced by the PV array into AC energy that is directly connected to the electric utility). In this paper, we present a simple method for detecting faults that occurred during the operation of the inverter. These types of faults or faults affect the efficiency and cost-effectiveness of the photovoltaic system, especially the inverter, which is the main component responsible for the conversion. Hence, we have shown first the faults obtained in the case of the short circuit. Second, the open circuit failure is studied. The results demonstrate the efficacy of the proposed method. Good monitoring and detection of faults in the inverter can increase the system's reliability and decrease the undesirable faults that appeared in the PV system. The system behavior is tested under variable parameters and conditions using MATLAB/Simulink.</p>

scholarly journals Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2782 ◽  
Author(s):  
Amith Khandakar ◽  
Muhammad E. H. Chowdhury ◽  
Monzure- Khoda Kazi ◽  
Kamel Benhmed ◽  
Farid Touati ◽  
...  
Keyword(s):  
Feature Selection ◽  
Output Power ◽  
Regression Models ◽  
Prediction Models ◽  
Gaussian Process Regression ◽  
Environmental Parameters ◽  
Ann Model ◽  
Power Prediction ◽  
Pv System ◽  
Energy Evaluation

Photovoltaics (PV) output power is highly sensitive to many environmental parameters and the power produced by the PV systems is significantly affected by the harsh environments. The annual PV power density of around 2000 kWh/m2 in the Arabian Peninsula is an exploitable wealth of energy source. These countries plan to increase the contribution of power from renewable energy (RE) over the years. Due to its abundance, the focus of RE is on solar energy. Evaluation and analysis of PV performance in terms of predicting the output PV power with less error demands investigation of the effects of relevant environmental parameters on its performance. In this paper, the authors have studied the effects of the relevant environmental parameters, such as irradiance, relative humidity, ambient temperature, wind speed, PV surface temperature and accumulated dust on the output power of the PV panel. Calibration of several sensors for an in-house built PV system was described. Several multiple regression models and artificial neural network (ANN)-based prediction models were trained and tested to forecast the hourly power output of the PV system. The ANN models with all the features and features selected using correlation feature selection (CFS) and relief feature selection (ReliefF) techniques were found to successfully predict PV output power with Root Mean Square Error (RMSE) of 2.1436, 6.1555, and 5.5351, respectively. Two different bias calculation techniques were used to evaluate the instances of biased prediction, which can be utilized to reduce bias to improve accuracy. The ANN model outperforms other regression models, such as a linear regression model, M5P decision tree and gaussian process regression (GPR) model. This will have a noteworthy contribution in scaling the PV deployment in countries like Qatar and increase the share of PV power in the national power production.

Output Characteristics Study of V-trough PV Concentration System

2011 ◽  
Vol 71-78 ◽  
pp. 2077-2080 ◽  
Author(s):  
Cui Qiong Yan
Keyword(s):  
Output Power ◽  
Short Circuit ◽  
Maximum Output Power ◽  
Open Circuit ◽  
Maximum Output ◽  
Water Cooling ◽  
Cell Array ◽  
Pv System ◽  
Current Output ◽  
Super Cell

A V-trough PV system with polysilicon cell array and super cell array has been constructed and tested. Open-circuit voltage, short-circuit current, output power, fill factor and influence of temperature on V-trough PV concentration system have been analyzed. The results indicate that the output power of 10 pieces of polysilicon cell array is 6.198W and it is 1.21 times as that of non-concentration condition. Maximum output power of V-trough PV system with water cooling increase to 8.28W and power increment rate reach 62.67% compared with the non-concentration PV system. For the super cell array with no water cooling, the maximum output power of V-trough PV system varies from 7.834W to 14.223W. The results of this work provide some experimental support to the applications of the V-trough PV system.

scholarly journals Fault detection and classification in Industrial IoT in case of missing sensor data

2021 ◽  
Author(s):  
Merim Dzaferagic ◽  
Nicola Marchetti ◽  
Irene Macaluso
Keyword(s):  
Fault Detection ◽  
Monitoring System ◽  
Sensor Data ◽  
Generative Adversarial Networks ◽  
Missing Measurements ◽  
Tennessee Eastman Process ◽  
Fault Detection And Classification ◽  
Industrial Iot ◽  
Critical Components ◽  
The Impact

This paper addresses the issue of reliability in Industrial Internet of Things (IIoT) in case of missing sensors measurements due to network or hardware problems. We propose to support the fault detection and classification modules, which are the two critical components of a monitoring system for IIoT, with a generative model. The latter is responsible of imputing missing sensor measurements so that the monitoring system performance is robust to missing data. In particular, we adopt Generative Adversarial Networks (GANs) to generate missing sensor measurements and we propose to fine-tune the training of the GAN based on the impact that the generated data have on the fault detection and classification modules. We conduct a thorough evaluation of the proposed approach using the extended Tennessee Eastman Process dataset. Results show that the GAN-imputed data mitigate the impact on the fault detection and classification even in the case of persistently missing measurements from sensors that are critical for the correct functioning of the monitoring system.

scholarly journals A Unified Approach for Analysis of Faults in Different Configurations of PV Arrays and Its Impact on Power Grid

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 156 ◽  
Author(s):  
Saba Gul ◽  
Azhar Ul Haq ◽  
Marium Jalal ◽  
Almas Anjum ◽  
Ihsan Ullah Khalil
Keyword(s):  
Thin Film ◽  
Short Circuit ◽  
Power Grid ◽  
Open Circuit ◽  
Fault Analysis ◽  
Operating Conditions ◽  
Power Grids ◽  
Pv System ◽  
Multiple Faults ◽  
The Impact

Fault analysis in photovoltaic (PV) arrays is considered important for improving the safety and efficiency of a PV system. Faults do not only reduce efficiency but are also detrimental to the life span of a system. Output can be greatly affected by PV technology, configuration, and other operating conditions. Thus, it is important to consider the impact of different PV configurations and materials for thorough analysis of faults. This paper presents a detailed investigation of faults including non-uniform shading, open circuit and short circuit in different PV interconnections including Series-Parallel (SP), Honey-Comb (HC) and Total-cross-Tied (TCT). A special case of multiple faults in PV array under non-uniform irradiance is also investigated to analyze their combined impact on considered different PV interconnections. In order to be more comprehensive, we have considered monocrystalline and thin-film PV to analyze faults and their impact on power grids. Simulations are conducted in MATLAB/Simulink, and the obtained results in terms of power(P)–voltage(V) curve are compared and discussed. It is found that utilization of thin-film PV technology with appropriated PV interconnections can minimize the impact of faults on a power grid with improved performance of the system.

scholarly journals Investigation of the energy efficiency of horizontally mounted solar module soiled with CaCo3

2017 ◽  
Vol 15 (2) ◽  
pp. 57-69 ◽  
Author(s):  
Ivana Radonjic ◽  
Tomislav Pavlovic
Keyword(s):  
Energy Efficiency ◽  
Calcium Carbonate ◽  
Fill Factor ◽  
Open Circuit Voltage ◽  
Short Circuit ◽  
Short Circuit Current ◽  
Open Circuit ◽  
Pv System ◽  
Solar Module ◽  
The Impact

Soiling is a term used to describe the deposition of dust (dirt) on solar modules, which reduces the amount of solar radiation reaching the solar cells. Deposition of dust on solar modules can make the operation of the entire PV system - more difficult and, therefore, lead to the generation of less electric energy. Soiling of solar modules also influences solar modules parameters (short-circuit current, open-circuit voltage, maximum power, fill factor and efficiency). This paper presents the results of the investigation on the impact different quantities of calcium carbonate (CaCO3) deposition have on the energy efficiency of horizontally mounted solar modules. The short-circuit current, power and efficiency decrease with increasing the mass of CaCO3 deposited on the horizontally mounted solar module. The open-circuit voltage and fill factor very slightly increase with increasing the mass of CaCO3 deposited on the horizontally mounted solar module. Upon soiling with 1 g of calcium carbonate, the solar module efficiency decreased by 4.6% in relation to the clean solar module, upon soiling with 2 g of calcium carbonate it decreased by 6.0%, and upon soiling with 3 g of calcium carbonate it decreased by 12.9% in relation to the clean solar module. It can be concluded that the power and energy efficiency of the solar module decrease due to the increased amount of calcium carbonate.

scholarly journals Machine Learning as an Efficient Diagnostic Tool for Fault Detection and Localization in Solar Photovoltaic Arrays

10.29007/34bz ◽  
2019 ◽  
Author(s):  
Masoud Alajmi ◽  
Sultan Aljahdali ◽  
Sultan Alsaheel ◽  
Mohammed Fattah ◽  
Mohammed Alshehri
Keyword(s):  
Machine Learning ◽  
Renewable Energy ◽  
Fault Detection ◽  
Solar Energy ◽  
Hot Spot ◽  
Low Cost ◽  
Short Circuit ◽  
Renewable Energy Sources ◽  
Open Circuit ◽  
Detection And Localization

Solar energy, one of many types of renewable energy, is considered to be an excellent alternative to non-renewable energy sources. Its popularity is increasing rapidly, especially because fuel energy consumes and depletes finite natural resources, polluting the environment, whereas solar energy is low- cost and clean. To produce a reliable supply of energy, however, solar energy must also be consistent. The energy we derive from a photovoltaic (PV) array is dependent on changeable factors such as sunlight, positioning of the array, covered area, and status of the solar cell. Every change adds potential for the creation of error in the array. Therefore, thorough research and a protocol for fast, efficient location and correction of all kinds of errors must be an urgent priority for researchers.For this project we used machine learning (ML) with voltage and current sensors to detect, localize and classify common faults including open circuit, short circuit, and hot-spot. Using the proposed algorithm, we have improved the accuracy of fault detection, classification and localization to 100%. Further, the proposed method can execute all three tasks (detection, classification, and localization) simultaneously.

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